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Skewness measures how asymmetric a data set's distribution is — a symmetric distribution (like a normal bell curve) has a skewness near zero, while distributions with a long tail on one side have a positive or negative skewness depending on which direction that tail points.
This calculator finds a data set's skewness and interprets what it means.
How it works
Enter your numbers as a comma-separated list. The calculator finds the mean and standard deviation, then applies the population (Fisher-Pearson) skewness formula, which compares the average cubed deviation from the mean to the cubed standard deviation.
- Enter numbers (comma-separated).
- Click Calculate to see your results.
Examples
A right-skewed data set
The data set 2, 4, 4, 5, 7, 9, 12, 15 has a skewness of about 0.61, indicating a mild right skew (a longer tail toward higher values).
Who should use it
- Checking skewness coursework for a statistics class.
- Understanding whether a data set is symmetric or skewed before choosing statistical methods that assume normality.
Industry applications
- Statistics and data science education
- Data analysis and quality control
Advantages
- Uses the standard, widely-taught Fisher-Pearson skewness formula.
- Provides a plain-language interpretation alongside the numeric result.
Limitations
- Uses the population (biased) skewness formula rather than a sample-adjusted variant — for most exploratory purposes this distinction is minor.
Common mistakes to avoid
- Confusing skewness (asymmetry direction) with variance or standard deviation (spread) — they measure different properties of a distribution.
- Over-interpreting small skewness values from a very small data set, where sample skewness can be noisy and unreliable.
Best practices
- Skewness is most meaningful with a reasonably sized data set — treat skewness from very small samples (under about 10 values) as a rough indication rather than a precise measurement.
Tips
- Many common statistical tests assume a roughly symmetric (normal-like) distribution — checking skewness first can flag when those assumptions might not hold for your data.